Remember banks a decade ago? The days when visiting your bank branch was mandatory to perform simple banking activities. Standing in a long queue for money withdrawal or deposit was a usual scene at any bank. Opening a deposit account was a long procedure. Loan approval was not so simple, too. And now, compare it with the present time. You will realize how banks have evolved and innovated. Emerging technological trends are changing the banking methods and experiences. A large part of this transformation is attributed to the latest technological field called Machine Learning. It is one of the dominant technological innovations changing the face of several industries and enterprises. The implications of machine learning in the banking industry are many, though it is in its nascent stage.
Machine learning has been a revolutionary force in the industry. However, experts admit in delay in the adoption of this leading technology. Since banking is a traditional industry, one of the biggest challenges it faces is reluctance towards change. The involvement of legacy systems and customers’ trust in them often challenge the integration process of the new technological innovations. Nonetheless, machine learning has become a technology of choice in banking and financial industry.
What is Machine Learning?
Machine learning is also often referred to as ML for convenience. It is a field of scientific study that studies the systems and models that provides computer systems the ability to learn, perform, and improve tasks without the need for explicit instructions. Based on inferences and historical patterns, ML is able to perform these tasks.
For example, on online shopping websites, you often see additional “similar products.” This is nothing but ML. Another classic example of ML is ads of products you searched on google, popping everywhere, even on your Gmail account. What about traffic prediction on GPS navigation services? It is machine learning at its best.
More examples include social media networks where you often stumble upon acquaintances on “People you may know” section. Face recognition, search engine result refining, speech recognition, and virtual assistants are all examples of machine learning. You may not even realize, but you are using this technology every day, quite frequently.
The primary aim of ML is to let computers learn on their own without human intervention and perform tasks without human assistance. These apps use a set of data and information to improve themselves. Today, financial services and banking industry are adopting this technology at a faster rate simply because customer experience and performance is at stake. Immense competition by FinTech has forced banks to make changes to stay ahead in the competition and relevant in the industry. They cannot risk the standard of their performance with such competition.
There are three types of Machine Learning:
- Supervised Learning:
As the name suggests, supervised learning is performed with the help of instructions. Machines use data and set of training examples that are termed as training data. This data set acts like a trainer or a guide and helps a model or machine to learn.
2. Unsupervised Learning:
In this form of machine learning, data that is not structured or labeled is used for learning. The machine draws inferences and patterns from the clustered dataset.
3. Reinforcement Learning:
It is a form of learning method wherein software interacts with its environment and discovers results to maximize the reward. It is a sort of a hit and miss method where the agent is rewarded for the correct answer and penalized for the wrong.
Artificial Intelligence (AI) vs Machine Learning
People often get confused between machine learning and artificial intelligence. Many times, they are thought to be the same. However, machine learning is a subset of artificial intelligence. Though they both are terms of computer science, there is a vast difference between the two.
As mentioned above, it is the ability of machines to learn without instructions. Meanwhile, Artificial Intelligence is the simulation of human cognitive abilities by machines. The primary goal of a computer program that works to eliminate human error and perform smart work. Meanwhile, ML is simply a concept wherein the machine learns from the given data set.
AI models are aimed to demonstrate natural intelligence abilities of humans such as problem-solving, decision making, and more. As for ML, the aim is to learn from the data and maximize task performance.
History of machine learning
History of machine learning can be traced back to the late 1950s. In 1959, Arthur Samuel, the pioneer in computer gaming and artificial intelligence coined the term machine learning. As a scientific field of artificial intelligence, machine learning grew. The research was conducted using simple algorithms. In the 1960s, a statistical inference in which Bayes theorem was created by using probabilistic inference in machine learning. During the AI winter, the research suffered. It bounced back only in the 1980s.
However, the major breakthroughs in the research came in the 90s. During this time, investigators began using a data-driven approach. Scientists now devoted time in creating programs to analyze large amounts of data and learn from it. 2006 witnessed the emergence of a new term ‘deep learning’, that explains the computer’s ability to see and distinguish. As soon as 2010 began, Microsoft Kinect was developed. Google Brain was born, and Google’s X Lab developed a machine algorithm. 2014, saw the birth of DeepFace, that recognize individuals on photos. Another machine learning platform was launched in 2015 and that was by the e-commerce giant Amazon. The growth of ML has been gradual, but it continues to evolve.
Benefits of Machine Learning in Banking
Just like Artificial Intelligence, machine learning has contributed massively to the development of the banking industry. The banks are rapidly adopting the technology for its various benefits. ML platforms make it possible to quickly and automatically analyze bigger and complex data and provide fast and accurate results.
Most industries working with a large volumes of data have recognized the benefits of ML and they are using it. Financial services, healthcare, government, retail, oil and gas, and transportation are some of the biggest industries making the best use of ML for their development.
- Machine learning software can easily rummage through the large volume of data and analyze the trends and patterns that may not be possible by human beings.
- Since the primary goal is to enable the learning process without explicit instructions, everything is automated and can be performed without human assistance.
- ML models are developed in a way that they strive to improve by interacting with their environment.
- No matter how diverse the data is, ML can handle it all.
- Machine learning programs provide quick results and real-time predictions.
- ML tools have the ability to save training costs
- It can be used to enhance the customer experience by providing them with personalized services.
- ML can enhance compliance with regulatory standards and improve productivity.
- Business gets a broader overview of the situation and a comprehensive assessment of risk.
- It provides a better understanding of present business issues.
- Ultimately, it facilitates better business processes.
Machine Learning in Banking
There are several ways machine applications in the banking industry have shown a drastic change. After realizing how FinTechs are using ML to their advantage against them, banks are now harnessing the power of ML to reduce operation cost, improve business, increase revenue, and perform efficiently within the regulatory policies. Learn how banks are using machine learning technology, below.
1. Improving business and increasing revenue:
There are several ways banks are using machine learning to increase their revenue. With the help of ML tools, they aim to optimize customer selection and strengthen their relationship with them. It allows them to create a better target of new customers, design new products and services that will attract and retain customers and strengthen the existing business models.
2. Enhanced customer experience:
Improving customer experience is the prime target of the banks. Machine learning is able to do what AI could not for customers. It provides a new dimension to AI chatbots and allows them to learn rather than simply following a set of instructions and provide better solutions to the customers.
3. Improve efficiency and productivity:
AI and ML have immensely improved the productivity and functioning of the banks in ways unimaginable. Today, machine learning applications in banking enable banks to perform market research, optimize inefficient loan approval processes, enhance call center operations, build and deploy business processes that are economical and faster.
4. Reduce risk:
Banks are increasingly deploying ML technology for better risk management. Not only does this software predict credit worthiness of the loan candidate, but it can also study market trend and how it affects the client’s ability to pay back the amount. Machine learning can provide solutions for several types of risk concerns. One of the greatest machine learning use cases in banking is Know Your Customer programs. That simplified several operations for banks.
5. Fraud detection and prevention:
Fraudulent and criminal activities are the biggest concern for banks. They must protect their clients from this and with Machine Learning in the banking industry, the war is won. It uses predictive analytics to detect fraudulent patterns and provide greater security.
6. Loan management and approval:
A growing number of banks have turned to machine learning for loan underwriting. With the help of predictive analysis and knowledge of historical patterns, it has reduced risk as well as huge paperwork that goes into loan management and approval. Verification and identification become a lot easier with machine learning.
Apart from this, machine learning in banking is being used to improve wealth management, trading and marketing, content interpretation, asset management, merchant services, and more. The new technology provides fresh and innovative ways of refining the products and services for their clients. While the machines can be incredibly powerful in improving banking activities, there are a few challenges it must tackle.
As industries continue to rapidly adopt the new technological trends, it is imperative that the businesses take time to do process mapping and provide proper training. A proper skill set must be developed to optimize the potential of the technology. The latest technological trend certainly does accelerate the efficiency of banks, an analytical approach will prevent the dangerous risks it may involve.
